We consider the interest of leveraging information between related tasks for learning Bayesian network structures. We propose a new algorithm called Multi-Task Max-Min Hill Climbing (MT-MMHC) that combines ideas from transfer learning, multi-task learning, constraintbased and search-and-score techniques. This approach consists in two main phases. The first one identifies the most similar tasks and uses their similarity to learn their corresponding undirected graphs. The second one directs the edges with a Greedy Search combined with a Branch-and-Bound algorithm. Empirical evaluation shows that MT-MMHC can yield better results than learning the structures individually or than the stateof-the-Art MT-GS algorithm in terms of structure learning accuracy and computational time.